CN113077136A - Intelligent distribution method, system and device for passenger rooms of mail steamer and storage medium - Google Patents

Intelligent distribution method, system and device for passenger rooms of mail steamer and storage medium Download PDF

Info

Publication number
CN113077136A
CN113077136A CN202110340005.9A CN202110340005A CN113077136A CN 113077136 A CN113077136 A CN 113077136A CN 202110340005 A CN202110340005 A CN 202110340005A CN 113077136 A CN113077136 A CN 113077136A
Authority
CN
China
Prior art keywords
neural network
characteristic information
user characteristic
network model
training
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202110340005.9A
Other languages
Chinese (zh)
Inventor
尹奇志
徐峰
袁裕鹏
朱晓晨
乔磊
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Wuhan University of Technology WUT
Original Assignee
Wuhan University of Technology WUT
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Wuhan University of Technology WUT filed Critical Wuhan University of Technology WUT
Priority to CN202110340005.9A priority Critical patent/CN113077136A/en
Publication of CN113077136A publication Critical patent/CN113077136A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/12—Hotels or restaurants

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Physics & Mathematics (AREA)
  • Tourism & Hospitality (AREA)
  • General Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Evolutionary Computation (AREA)
  • General Health & Medical Sciences (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • General Engineering & Computer Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Artificial Intelligence (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Biology (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Software Systems (AREA)
  • Mathematical Physics (AREA)
  • Computing Systems (AREA)
  • Molecular Biology (AREA)
  • Primary Health Care (AREA)
  • Computational Linguistics (AREA)
  • Biophysics (AREA)
  • Development Economics (AREA)
  • Educational Administration (AREA)
  • Biomedical Technology (AREA)
  • Game Theory and Decision Science (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention relates to an intelligent distribution method, a system, a device and a storage medium for passenger rooms of a mail steamer, wherein the method comprises the following steps: acquiring user characteristic information, preprocessing the user characteristic information to obtain preprocessed data, and enabling the preprocessed data to form a data set; constructing a neural network, and training and verifying a neural network model according to the data set to obtain a neural network model based on guest room distribution; and re-collecting the user characteristic information, inputting the re-collected user characteristic information into a neural network model based on guest room distribution, and obtaining a guest room distribution result. The intelligent passenger room distribution method for the mail steamer provided by the invention realizes reasonable passenger room distribution according to the demands of user groups.

Description

Intelligent distribution method, system and device for passenger rooms of mail steamer and storage medium
Technical Field
The invention relates to the technical field of guest room distribution, in particular to an intelligent distribution method, system and device for passenger rooms of a mail steamer and a computer readable storage medium.
Background
The existing hotel room reservation mode mainly comprises telephone reservation and online reservation, and the rooms of a mail steamer cannot be reserved in advance; large cruise ships tend to have many rooms up to a dozen decks, and rooms at different heights or areas may have different water supply flow rates, pressures and temperatures, and people of different ages and gender sizes may have different water comfort requirements (flow rates, pressures, temperatures).
However, the existing passenger room distribution of the mail ship generally adopts manual distribution, and the passenger room distribution can not be reasonably distributed according to the requirements of different users, so that the customer distribution efficiency is influenced.
Disclosure of Invention
In view of the above, there is a need to provide an intelligent method, system, device and computer readable storage medium for allocating passenger rooms of a mail ship, so as to solve the problem that the prior art cannot reasonably allocate the passenger rooms according to the demands of users.
The invention provides an intelligent distribution method of passenger rooms of a mail steamer, which comprises the following steps:
acquiring user characteristic information, preprocessing the user characteristic information to obtain preprocessed data, and enabling the preprocessed data to form a data set;
constructing a neural network, and training and verifying a neural network model according to the data set to obtain a neural network model based on guest room distribution;
and re-collecting the user characteristic information, inputting the re-collected user characteristic information into a neural network model based on guest room distribution, and obtaining a guest room distribution result.
Further, the user characteristic information comprises user crowd characteristic information and comfortable water use condition information.
Further, the preprocessing the user feature information specifically includes: and removing abnormal data in the user characteristic information, normalizing the field values in the user characteristic information, and removing repeated user characteristic information.
Further, forming the preprocessed data into a data set specifically includes: and dividing the preprocessed data into a training verification set and a test set according to a set proportion to form a data set.
Further, constructing a neural network specifically includes: and performing feature extraction on the data set as input through a backbone network, and constructing a neural network model by taking the maximum data length of the data set as the number of nodes of a full-connection layer.
Further, training and verifying the neural network model according to the training verification set and the test set specifically include: and dividing the training and verifying set into a training set and a verifying set by using cross verification, performing iterative training on the neural network model by using the training set and the testing set, and verifying by using the verifying set.
Further, the intelligent mail steamer room distribution method also comprises the step of pushing the room distribution result to the corresponding user.
The invention also provides an intelligent passenger room distribution system for the mail steamer, which comprises a data set acquisition module, a neural network model acquisition module and a passenger room distribution module;
the data set acquisition module is used for acquiring user characteristic information, preprocessing the user characteristic information to obtain preprocessed data and enabling the preprocessed data to form a data set;
the neural network model acquisition module is used for constructing a neural network, training and verifying the neural network model according to the data set and obtaining a neural network model based on guest room distribution;
the guest room distribution module is used for re-collecting the user characteristic information, inputting the re-collected user characteristic information into a neural network model based on guest room distribution, and obtaining a guest room distribution result.
The invention also provides an intelligent distribution device for passenger rooms of a mail ship, which comprises a processor and a memory, wherein the memory is stored with a computer program, and when the computer program is executed by the processor, the intelligent distribution method for the passenger rooms of the mail ship is realized according to any one of the technical schemes.
The embodiment of the invention also provides a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and when the computer program is executed by a processor, the method for intelligently allocating passenger rooms of the mail ship is realized according to any technical scheme.
Compared with the prior art, the invention has the beneficial effects that: preprocessing the user characteristic information by acquiring the user characteristic information to obtain preprocessed data, and enabling the preprocessed data to form a data set; constructing a neural network, and training and verifying a neural network model according to the data set to obtain a neural network model based on guest room distribution; re-collecting the user characteristic information, inputting the re-collected user characteristic information into a neural network model based on guest room distribution, and obtaining a guest room distribution result; the guest rooms can be reasonably distributed according to the demands of user groups.
Drawings
FIG. 1 is a schematic flow chart of an intelligent mail steamer room allocation method provided by the present invention;
fig. 2 is a block diagram of the intelligent distribution system for passenger rooms of a mail steamer.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
Example 1
The embodiment of the invention provides an intelligent allocation method for passenger rooms of a mail ship, which is a flow schematic diagram, and as shown in figure 1, the intelligent allocation method for the passenger rooms of the mail ship comprises the following steps:
s1, acquiring user characteristic information, preprocessing the user characteristic information to obtain preprocessed data, and enabling the preprocessed data to form a data set;
s2, constructing a neural network, and training and verifying a neural network model according to the data set to obtain a neural network model based on guest room distribution;
and S3, re-collecting the user characteristic information, inputting the re-collected user characteristic information into a neural network model based on the guest room distribution, and obtaining a guest room distribution result.
The technical scheme of the invention realizes reasonable guest room distribution according to the demands of user groups.
Preferably, the user characteristic information includes user crowd characteristic information and comfortable water use condition information.
In one embodiment, the user population characteristic information includes gender, age (children, teenagers, middle-aged people and elderly people), height and the like, and the comfortable water use condition information includes that the winter comfortable water temperature of the young men is about 39.2 ℃ and the comfortable flow rate is about 10.3L/min; the comfortable water temperature of young women is about 39.3 ℃, and the flow rate is about 11.0L/min; the comfortable water temperature of the middle-aged and the elderly men is about 40.8-41 ℃, and the flow rate is about 9.4L/min.
In another embodiment, the comfortable water condition information acquisition means comprises document review, questionnaire survey, network data and the like; the age can be divided into stages below 10 years old, 10-20, 20-40, 40-60 and above 60 years old; sex is divided into male and female; the height can be divided into 1m below, 1-1.5m, 1.5-1.7, 1.7-1.8, 1.8 above, etc.; different water consumption comfortable flow, temperature and pressure of users with different sexes, ages, heights and weights can be collected through the database.
Preferably, the preprocessing the user feature information specifically includes: and removing abnormal data in the user characteristic information, normalizing the field values in the user characteristic information, and removing repeated user characteristic information.
It should be noted that if there is abnormal data in the user feature information, for example, if the data is obviously too large or too small, the data needs to be removed, and by normalizing the field values in the user feature information, the data is conveniently used for training the neural network model; in addition, user characteristic information needs to be removed.
Preferably, the forming the preprocessed data into a data set specifically includes: and dividing the preprocessed data into a training verification set and a test set according to a set proportion to form a data set.
Preferably, the constructing the neural network specifically includes: and performing feature extraction on the data set as input through a backbone network, and constructing a neural network model by taking the maximum data length of the data set as the number of nodes of a full-connection layer.
In a specific embodiment, the neural network may be a convolutional neural network, the input and the output may be connected in series in the network, and the number of series connections at different positions in the backbone network may be different. The convolution module in the backbone network mainly has the functions of feature extraction and feature dimension increasing to obtain more useful features; the input layer of the convolutional neural network can process multidimensional data; the convolutional neural network is similar to the neural network, and since learning is performed using a gradient descent algorithm, input characteristics of the convolutional neural network need to be standardized.
In another specific embodiment, a 6-layer convolutional neural network model is constructed, and the structure of the model sequentially comprises a first convolutional layer, a second convolutional layer, a first down-sampling layer, a first full-connection layer and a first dropout layer arch and a second full-connection layer. Setting parameters of each layer in the convolutional neural network model as follows, wherein the total number of feature maps of input layers of the convolutional neural network is set to be 3, and the size of the feature maps is set to be 64 multiplied by 64; setting the total number of convolution filters in the first convolution layer to 32, the pixel size of the convolution filter to 3 × 3, the feature map size to 64 × 64, and the convolution step size to 1 pixel; the total number of convolution filters in the second convolution layer was set to 32, the pixel size of the convolution filter was set to 3 × 3, the feature map size was set to 64 × 64, and the convolution step size was set to 1 pixel. The pooling region size in the first downsampling layer is set to 2 × 2, the pooling step size is set to 2 pixels, and the feature map size is set to 32 × 32.
Preferably, training and verifying the neural network model according to the training verification set and the test set specifically include: and dividing the training and verifying set into a training set and a verifying set by using cross verification, performing iterative training on the neural network model by using the training set and the testing set, and verifying by using the verifying set.
It should be noted that, by dividing the training verification set into a training set and a verification set by using cross validation, the accuracy of the neural network model based on room allocation can be obtained; thereby improving the accuracy of room allocation.
In one specific embodiment, thousands of data are set, the data are randomly scrambled, the data are divided into a training and testing set, the training and testing set is divided into a set number of parts by using multiple times of cross validation, and a plurality of models are trained by using the training and testing set as the validation set each time a plurality of parts of training and testing set are used as the training set; setting the number of iterations of the training batch size meter; the final evaluation index of the network is the average value of F1 scores of each type, and the probability average value of a plurality of test results of the model pair test set obtained by multiple times of cross validation is used as a final result;
Figure BDA0002998877000000061
p is accuracy and R is recall.
Preferably, the method for intelligently allocating passenger rooms of the mail ship further comprises the step of pushing the result of the passenger room allocation to the corresponding user.
It should be noted that the user may autonomously select to accept the room allocation result or not accept the room allocation result.
Example 2
The embodiment of the invention provides an intelligent distribution system of passenger rooms of a mail-carrier, which has a structural block diagram, as shown in fig. 2, and comprises a data set acquisition module 1, a neural network model acquisition module 2 and a passenger room distribution module 3; the data set acquisition module 1, the neural network model acquisition module 2 and the guest room distribution module 3 are sequentially connected;
the data set acquisition module 1 is configured to acquire user characteristic information, preprocess the user characteristic information to obtain preprocessed data, and enable the preprocessed data to form a data set;
the neural network model obtaining module 2 is used for constructing a neural network, training and verifying the neural network model according to the data set, and obtaining a neural network model based on guest room distribution;
the guest room distribution module 3 is configured to re-collect the user characteristic information, input the re-collected user characteristic information to a neural network model based on guest room distribution, and obtain a guest room distribution result.
Example 3
The embodiment of the invention provides an intelligent distribution device for passenger rooms of a passenger liner, which comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the intelligent distribution method for the passenger rooms of the passenger liner of the passenger.
Example 4
An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for intelligently allocating passenger rooms of a mail ship according to embodiment 1.
The invention discloses a mail-carrier guest room intelligent distribution method, a system, a device and a computer readable storage medium, wherein the method comprises the steps of obtaining user characteristic information, preprocessing the user characteristic information to obtain preprocessed data, and enabling the preprocessed data to form a data set; constructing a neural network, and training and verifying a neural network model according to the data set to obtain a neural network model based on guest room distribution; re-collecting the user characteristic information, inputting the re-collected user characteristic information into a neural network model based on guest room distribution, and obtaining a guest room distribution result; the guest rooms can be reasonably distributed according to the demands of user groups.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.

Claims (10)

1. An intelligent distribution method for passenger rooms of a mail steamer is characterized by comprising the following steps:
acquiring user characteristic information, preprocessing the user characteristic information to obtain preprocessed data, and enabling the preprocessed data to form a data set;
constructing a neural network, and training and verifying a neural network model according to the data set to obtain a neural network model based on guest room distribution;
and re-collecting the user characteristic information, inputting the re-collected user characteristic information into a neural network model based on guest room distribution, and obtaining a guest room distribution result.
2. The intelligent mail ship room distribution method of claim 1, wherein the user characteristic information comprises user crowd characteristic information and comfort water condition information.
3. The intelligent mail steamer room allocation method as claimed in claim 1, wherein the preprocessing of the user characteristic information specifically comprises: and removing abnormal data in the user characteristic information, normalizing the field values in the user characteristic information, and removing repeated user characteristic information.
4. The intelligent mail ship room allocation method of claim 1, wherein the forming of the preprocessed data into a data set specifically comprises: and dividing the preprocessed data into a training verification set and a test set according to a set proportion to form a data set.
5. The intelligent mail steamer room allocation method as claimed in claim 1, wherein the building of the neural network specifically comprises: and performing feature extraction on the data set as input through a backbone network, and constructing a neural network model by taking the maximum data length of the data set as the number of nodes of a full-connection layer.
6. The intelligent mail carrier room allocation method of claim 4, wherein training and verifying a neural network model according to the training verification set and the test set specifically comprises: and dividing the training and verifying set into a training set and a verifying set by using cross verification, performing iterative training on the neural network model by using the training set and the testing set, and verifying by using the verifying set.
7. The intelligent mail ship room allocation method as recited in claim 1, further comprising pushing room allocation results to corresponding users.
8. An intelligent passenger room distribution system for a passenger ship is characterized by comprising a data set acquisition module, a neural network model acquisition module and a passenger room distribution module;
the data set acquisition module is used for acquiring user characteristic information, preprocessing the user characteristic information to obtain preprocessed data and enabling the preprocessed data to form a data set;
the neural network model acquisition module is used for constructing a neural network, training and verifying the neural network model according to the data set and obtaining a neural network model based on guest room distribution;
the guest room distribution module is used for re-collecting the user characteristic information, inputting the re-collected user characteristic information into a neural network model based on guest room distribution, and obtaining a guest room distribution result.
9. An intelligent passenger compartment allocation device for a passenger liner, comprising a processor and a memory, wherein the memory stores a computer program, and the computer program is executed by the processor to realize the intelligent passenger compartment allocation method for a passenger liner according to any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method for intelligent allocation of mail-ship rooms as claimed in any one of claims 1 to 7.
CN202110340005.9A 2021-03-30 2021-03-30 Intelligent distribution method, system and device for passenger rooms of mail steamer and storage medium Pending CN113077136A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110340005.9A CN113077136A (en) 2021-03-30 2021-03-30 Intelligent distribution method, system and device for passenger rooms of mail steamer and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110340005.9A CN113077136A (en) 2021-03-30 2021-03-30 Intelligent distribution method, system and device for passenger rooms of mail steamer and storage medium

Publications (1)

Publication Number Publication Date
CN113077136A true CN113077136A (en) 2021-07-06

Family

ID=76611530

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110340005.9A Pending CN113077136A (en) 2021-03-30 2021-03-30 Intelligent distribution method, system and device for passenger rooms of mail steamer and storage medium

Country Status (1)

Country Link
CN (1) CN113077136A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115496246A (en) * 2022-10-08 2022-12-20 卓思韦尔(北京)信息技术有限公司 Group difference-based intelligent searching and agile distribution method for shared conference room

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101540034A (en) * 2008-03-21 2009-09-23 当代天启技术（北京）有限公司 Room management system and room management method
CN108628218A (en) * 2018-05-31 2018-10-09 深圳春沐源控股有限公司 room management method and device
CN111310952A (en) * 2020-05-13 2020-06-19 北京三快在线科技有限公司 Room reservation information processing method, room reservation system and server
CN112163491A (en) * 2020-09-21 2021-01-01 百度在线网络技术（北京）有限公司 Online learning method, device, equipment and storage medium

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101540034A (en) * 2008-03-21 2009-09-23 当代天启技术（北京）有限公司 Room management system and room management method
CN108628218A (en) * 2018-05-31 2018-10-09 深圳春沐源控股有限公司 room management method and device
CN111310952A (en) * 2020-05-13 2020-06-19 北京三快在线科技有限公司 Room reservation information processing method, room reservation system and server
CN112163491A (en) * 2020-09-21 2021-01-01 百度在线网络技术（北京）有限公司 Online learning method, device, equipment and storage medium

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115496246A (en) * 2022-10-08 2022-12-20 卓思韦尔(北京)信息技术有限公司 Group difference-based intelligent searching and agile distribution method for shared conference room

Similar Documents

Publication Publication Date Title
CN101419671B (en) Face gender identification method based on fuzzy support vector machine
CN106485262A (en) A kind of bus load Forecasting Methodology
CN110353675A (en) The EEG signals emotion identification method and device generated based on picture
CN108509607A (en) A kind of community discovery method and system based on Louvain algorithms
CN109242260B (en) Construction method and verification method of landscape ecological vulnerability index
CN112330028A (en) Electric bus charging load prediction method based on spectral clustering and LSTM neural network
CN106203473B (en) A kind of mobile subscriber's gender prediction's method based on installation the package list
CN107679539B (en) Single convolution neural network local information and global information integration method based on local perception field
CN109740734B (en) Image classification method of convolutional neural network by optimizing spatial arrangement of neurons
CN108960488A (en) A kind of accurate prediction technique of saturation loading spatial distribution based on deep learning and Multi-source Information Fusion
CN109767836A (en) A kind of medical diagnosis artificial intelligence system, device and its self-teaching method
CN113077136A (en) Intelligent distribution method, system and device for passenger rooms of mail steamer and storage medium
CN106846082A (en) Tourism cold start-up consumer products commending system and method based on hardware information
CN111626321A (en) Image data clustering method and device
CN113822419A (en) Self-supervision graph representation learning operation method based on structural information
CN114880538A (en) Attribute graph community detection method based on self-supervision
CN104361339A (en) Palm image extracting and identification method
CN109410074A (en) Intelligent core protects method and system
CN112819208A (en) Spatial similarity geological disaster prediction method based on feature subset coupling model
CN111709441A (en) Behavior recognition feature selection method based on improved feature subset discrimination
CN116740485A (en) Training method and device for lesion detection model, electronic equipment and storage medium
CN116704231A (en) Fine granularity image classification method for double-layer component distillation
CN112488936B (en) Method for deblurring finger vein blurred image based on generation countermeasure network
CN110659823A (en) Similar watershed analysis method, model, system and computer storage medium
CN114707044B (en) Method and system for extracting collective social behavior based on community discovery

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20210706